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Improved multi-objective clustering algorithm using particle swarm optimization
Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clusteri...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716574/ https://www.ncbi.nlm.nih.gov/pubmed/29206880 http://dx.doi.org/10.1371/journal.pone.0188815 |
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author | Gong, Congcong Chen, Haisong He, Weixiong Zhang, Zhanliang |
author_facet | Gong, Congcong Chen, Haisong He, Weixiong Zhang, Zhanliang |
author_sort | Gong, Congcong |
collection | PubMed |
description | Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. |
format | Online Article Text |
id | pubmed-5716574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57165742017-12-15 Improved multi-objective clustering algorithm using particle swarm optimization Gong, Congcong Chen, Haisong He, Weixiong Zhang, Zhanliang PLoS One Research Article Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. Public Library of Science 2017-12-05 /pmc/articles/PMC5716574/ /pubmed/29206880 http://dx.doi.org/10.1371/journal.pone.0188815 Text en © 2017 Gong et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gong, Congcong Chen, Haisong He, Weixiong Zhang, Zhanliang Improved multi-objective clustering algorithm using particle swarm optimization |
title | Improved multi-objective clustering algorithm using particle swarm optimization |
title_full | Improved multi-objective clustering algorithm using particle swarm optimization |
title_fullStr | Improved multi-objective clustering algorithm using particle swarm optimization |
title_full_unstemmed | Improved multi-objective clustering algorithm using particle swarm optimization |
title_short | Improved multi-objective clustering algorithm using particle swarm optimization |
title_sort | improved multi-objective clustering algorithm using particle swarm optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716574/ https://www.ncbi.nlm.nih.gov/pubmed/29206880 http://dx.doi.org/10.1371/journal.pone.0188815 |
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